Generic Machine Learning

Generic machine learning (GML) focuses on developing algorithms and frameworks applicable across diverse machine learning tasks and datasets, aiming for improved efficiency and broader applicability. Current research emphasizes enhancing explainability through formalizing feature importance and developing methods for simultaneous learning and evaluation, alongside exploring the use of GML in diverse applications such as education (e.g., using large language models for course support), robotics (e.g., developing adaptable locomotion control), and scientific computing (e.g., accelerating solutions to partial differential equations). The development of robust and efficient GML techniques holds significant potential for advancing various scientific fields and improving the performance and reliability of machine learning systems in real-world applications.